10000 fps 🚀 for 360 Surround-View CUDA Solution

nndeploy是一款最新上线的支持多平台、简单易用、高性能的机器学习部署框架，一套实现可在多端(云、边、端)完成模型的高性能部署。

Fine-tune SAM(Segment Anything Model) for class-aware computer vision tasks in specific scenarios

Segment Anything in High Quality

The official repository for MedSAM: Segment Anything in Medical Images.

This is the official repository for our recent work: PIDNet

OpenMMLab Detection Toolbox and Benchmark

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Implementation of popular deep learning networks with TensorRT network definition API

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

YOLOv6: a single-stage object detection framework dedicated to industrial applications.

Convolutional recurrent network in pytorch

🔥 比libevent、libuv更易用的国产网络库。A c/c++ network library for developing TCP/UDP/SSL/HTTP/WebSocket/MQTT client/server.

基于Yolov5车牌检测,更快更准.

multi-task yolov5 with detection and segmentation

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

End-to-End Object Detection with Transformers

A pytorch re-implementation of Real-time Scene Text Detection with Differentiable Binarization

超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , dbnet(1.7M) + crnn(6.3M) + anglenet(1.5M) 总模型仅10M

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

SLAM 开发学习资源与经验分享

Implements Sketch-a-Net in PyTorch and trains it with QuickDraw raster images

[python3.6] 运用tf实现自然场景文字检测,keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

CRNN with attention to do OCR,add Chinese recognition

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记